Cybernetist

When working on my last project I needed to find a way to draw a random number from a list based on some weight assigned to it i.e. given a list of numbers each of which has a weight assigned to it, I had to find a way to draw a number from the list based on the weight. The numbers which have higher weight assigned to them should be more likely to be drawn than the numbers with lower weights.
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Before the end of the last year I went to see a movie made by one of my favourite movie directors, Damien Chazelle. The movie is called First Man and it tells the story of the journey of the first man on the Moon. I am also a bit of a space nerd, so my expectations were sky high. I was happy to find out the movie delivered on its promise.
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This summer I spent quite a bit of time speaking to various people about “intelligent” Edge computing. I put double quotes around the word intelligent to avoid the wrath of the thought leaders on the internet as they fight each other over what intelligence is and what is not.
The more I talked to people the more I was realizing the possibilities and opportunities the edge computing opens up for the future: “injecting” intelligence into dumb physical objects seems akin to injecting “life” to dead objects, at least for us, scifi fans, which makes up for interesting vision of the future (for brevity let’s ignore the IoT security issues in this post).
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Tensorflow conditionals and while loops Recntly I found myself needing to implement more advanced control flow in some models I have been hacking on in my free time. In past I never really needed any graph conditionals or loops or any combinations thereof, so I had to dive into documentation and read up on them.
This blog post covers tf.cond and tf.while_loop control flow operations and was written to document and share my experience learning about them.
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As I continue to explore the realm of Artificial Neural Networks (ANN) I keep learning about some really cool types of neural networks which don’t get much attention these days. Many of these networks have not found much practical use in the “real world” for various reasons. Nevertheless, I find learning about them in my free time somewhat stimulating as they often make me think of some problems from a different perspective.
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I haven’t blogged here for over 2 years. It’s not that I had nothing to say, but every time I started writing a new post I never pushed myself into finishing it. So, most of the drafts ended up rotting in my private Github gists. Although my interests have expanded way beyond the Linux container space, my professional life remained tied to it.
Over the past two years I have been quite heavily involved in Kubernetes (K8s) community.
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Couple of months ago I came across a type of Artificial Neural Network I knew very little about: Self-organizing map (SOM). I vaguely remembered the term from my university studies. We scratched upon it when we were learning about data clustering algorithms. So when I re-discovered it again, my knowledge of it was very basic, almost non-existent. It felt like a great opportunity to learn something new and interesting, so I rolled up my sleeves, dived into reading and hacking.
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My rekindled interest in Machine Learning turned my attention to Neural Networks or more precisely Artificial Neural Networks (ANN). I started tinkering with ANN by building simple prototypes in R. However, my basic knowledge of the topic only got me so far. I struggled to understand why certain parameters work better than others. I wanted to understand the inner workings of ANN learning better. So I built a long list of questions and started looking for answers.
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This is the second post of the series about Principal Component Analysis (PCA). Whilst the first post provided a theoretical background, this post will discuss the actual implementation of the PCA algorithm and its results when applied to some example data.
Theory Recap In the first post we learnt that PCA looks for a vector basis that can express the analysed data in a better (less redundant) way, whilst retaining as much information from the original data as possible.
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This is the first of the two post series about Principal Component Analysis (PCA). This post lays down important knowledge bricks that are needed to understand the core principles of the PCA algorithm. The second post will discuss the actual implementation and its results by applying it to various data sets.
Motivation The world is becoming more data driven than ever before. We collect large amounts of data from arbitrary sources.
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